WHat we offer
Data Driven Forecasts
Assumptions are the primary factor for the decisions made by most organization, while good forecasting insights are often not considered or as much a looked at.
Forecasts from the Cloud
SciScry offers access to the most advanced forecasting algorithms available. Customers connect their data to our cloud solution and generate meaningful and accurate forecasts for their most important KPIs.
3rd PARTY DATA
Relevant 3rd party information can easily be integrated as domain knowledge to boost the forecasting performance of the algorithms.
Building on AI, machine learning as well as statistical methods, SciScry offers all relevant forecasting approaches and automatically selects the most advantageous model for each data set.
MLaaS OR ON-PREMISE
Our solution is available in the cloud as machine learning as a service (MLaaS) or as on-premise installation.
Data Science requires lots of data cleansing and preparation for machine learning. Our solution takes over most of the work.
3rd Party Data
Numerous interfaces to 3rd party data platforms allow the integration of external data that help boost the accuracy of the models.
Bring your data
To utilize the power of machine learning, all you need is to bring your data. We do the rest.
We train multiple models and measure which ones are best to describe and predict your data.
To enable seamless integration into your enterprise environment we offer a flexible API that allows you to integrate our forecasting results seamlessly.
Machines and machine learning algorithms are a great way to complement the strengths of human intelligence. Below you find a short list at what people and machines excel at.
|Comprehending / Expressing||Imagining|
|Number Crunching||Deductive Reasoning|
|Documenting / Organizing||Structuring Problem-Solving|
for data driven forecasts
Here are a few examples what the SciScry solution is applied for.
As CEO Alek needs to provide accurate forecasts of the revenue growth of his public shampoo selling conglomerate to analysts during annual general meetings and quarterly earnings calls.
In the past his forecasts where based on the best knowledge of his staff, but not necessarily validated by the patterns and trends hidden inside the historical corporate data.
Being outside of the guidance provided through official investor communication:
- Hurts the companies stock price
- Creates mistrust and
- Harms investors confidence
Implement the SciScry forecasting solution identifying and fine-tuning the best-suited models that describe datas patterns and trends optimally. As outcome, Alek not only receives an unbiased forecasts but confidence intervals that allow him to provide upper and lower bounds for his guidance. With his CEO superpower of human intuition he can argue the plausibility of each scenario.
As Sales Forecaster Oli has the challenge to provide an accurate forecast to the Finance and Procurement per store affected by numerous local factors.
In the past her forecasts where primarily based on the inputs from the staff in the stores.
They are not well-trained in statistics, rely on their local data only and are incentivized by how much they achieve compared to their forecast.
Failing to provide accurate forecasts leads to:
- missed sales opportunities,
- reduced customer satisfaction and
- negative financial consequences
Overestimations negatively impact working capital and profitability.
To low estimates generate unmet demand benefiting the competition.
With SciScry the relevant drivers in the historical data from Retail Inc. are identified and a more accurate forecast on store level is created.
In consequence Procurement has a better understanding of the required inventory for each store and the Finance department has more flexibility in the usage of their financial resources.
As part of the crew scheduling team Liev is responsible for the standby crew planning at Fly cheap. Aside from fuel personnel is the biggest cost for an airline.
He must decide how many standby crew members will be required for any given day for the various airports, aircraft types and crew member types.
Being wrong on can have severe implications for the profitability of the airline. If not enough crew is put on standby and a flights to be cancelled, incurring significant costs for airlines. Planning to much crew incurs non-productive costs that also negatively impacts the bottom-line.
SciScry can take corporate absenteeism data and create predictions based on the seasonal, yearly and weekly patterns that provide a more accurate view at which point how much standby crew is required.
With his data being his guide Liev spots the different trends and can decide where he may be willing to take more risks and where he wants to play it safe.
Ph.D. in extraterrestrial physics with a strong data science background. At SciScry he focuses on the implementation of machine learning algorithms as well as the system architecture.
An experienced entrepreneur and former consultant optimizing planning and forecasting processes at large enterprises. At SciScry he focuses on all business aspects.
Ph.D. in theoretical particle physics with a strong data science background. At SciScry he focuses on the implementation of machine learning algorithms and model validation.